OpenRaiser/Intern-Atlas huggingface dataset:
Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering
Venue: arXiv cs.LG 2025
Year: 2025

Accurate spectrum prediction is crucial for dynamic spectrum access (DSA) and resource allocation. However, due to the unique characteristics of spectrum data, existing methods based on the time or frequency domain often struggle to separate predictable patterns from noise. To address this, we propose the Spectral Fractional Filtering and Prediction (SFFP) framework. SFFP first employs an adaptive fractional Fourier transform (FrFT) module to transform spectrum data into a suitable fractional Fourier domain, enhancing the separability of predictable trends from noise. Subsequently, an adaptive Filter module selectively suppresses noise while preserving critical predictive features within this domain. Finally, a prediction module, leveraging a complex-valued neural network, learns and forecasts these filtered trend components. Experiments on real-world spectrum data show that the SFFP outperforms leading spectrum and general forecasting methods.
Edge-Enhanced Vision Transformer Framework for Accurate AI-Generated Image Detection
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement of generative models has led to a growing prevalence of highly realistic AI-generated images, posing significant challenges for digital forensics and content authentication. Conventional detection methods mainly rely on deep learning models that extract global features, which often overlook subtle structural inconsistencies and demand substantial computational resources. To address these limitations, we propose a hybrid detection framework that combines a fine-tuned Vision Transformer (ViT) with a novel edge-based image processing module. The edge-based module computes variance from edge-difference maps generated before and after smoothing, exploiting the observation that AI-generated images typically exhibit smoother textures, weaker edges, and reduced noise compared to real images. When applied as a post-processing step on ViT predictions, this module enhances sensitivity to fine-grained structural cues while maintaining computational efficiency. Extensive experiments on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that the proposed framework achieves superior detection performance across all benchmarks, attaining 97.75% accuracy and a 97.77% F1-score on CIFAKE, surpassing widely adopted state-of-the-art models. These results establish the proposed method as a lightweight, interpretable, and effective solution for both still images and video frames, making it highly suitable for real-world applications in automated content verification and digital forensics.
modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring
Venue: arXiv cs.SE 2025
Year: 2025

The development of advanced software tools for power system analysis requires extensive programming expertise. Even when using open-source tools, programming skills are essential to modify built-in models. This can be particularly challenging for domain experts who lack coding proficiency. This paper introduces modelSolver, a software solution with a new framework centered around symbolic mathematical modeling. The proposed paradigm facilitates defining models through intuitive mathematical expressions, thus eliminating the need for traditional programming constructs such as arrays, loops, and sparse matrix computations. The modelSolver focuses on power flow and state estimation using an open-box approach, which allows users to specify custom models using either real or complex variables. Unlike existing tools that rely on hard-coded models, modelSolver enables the representation of a wide range of advanced functionalities, including power flow with voltage regulators and load tap changers, continuation power flow, and Gauss-Newton state estimation with equality constraints. Compatibility with MATPOWER is ensured via a converter that automates importing data files. The framework prioritizes model-driven development and empowers domain experts to focus on power system modeling without programming barriers. It aims to simplify power system computations, making them more accessible to students, scientists, and practitioners.
PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs
Venue: arXiv cs.DB 2025
Year: 2025

Approximate Nearest Neighbor Search (ANNS) plays a crucial role in many key areas. Proximity graphs (PGs) are the leading method for ANNS, offering the best balance between query efficiency and accuracy. However, their performance heavily depends on various construction and query parameters, which are difficult to optimize due to their complex inter-dependencies. Given that users often prioritize specific accuracy levels, efficiently identifying the optimal PG configurations to meet these targets is essential. Although some studies have explored automatic configuration tuning for PGs, they are limited by inefficiencies and suboptimal results. These issues stem from the need to construct numerous PGs for searching and re-tuning from scratch whenever the dataset changes, as well as the failure to capture the complex dependencies between configurations, query performance, and tuning objectives. To address these challenges, we propose PGTuner, an efficient framework for automatic PG configuration tuning leveraging pre-training knowledge and model transfer techniques. PGTuner improves efficiency through a pre-trained query performance prediction (QPP) model, eliminating the need to build multiple PGs. It also features a deep reinforcement learning-based parameter configuration recommendation (PCR) model to recommend optimal configurations for specific datasets and accuracy targets. Additionally, PGTuner incorporates out-of-distribution detection and deep active learning for efficient tuning in dynamic scenarios and transferring to new datasets. Extensive experiments demonstrate that PGTuner can stably achieve the top-level tuning effect across different datasets while significantly improving tuning efficiency by up to 14.69X, with a 14.64X boost in dynamic scenarios. The code and data for PGTuner are available online at https://github.com/hao-duan/PGTuner.
UniAPO: Unified Multimodal Automated Prompt Optimization
Venue: arXiv cs.CV 2025
Year: 2025

Prompting is fundamental to unlocking the full potential of large language models. To automate and enhance this process, automatic prompt optimization (APO) has been developed, demonstrating effectiveness primarily in text-only input scenarios. However, extending existing APO methods to multimodal tasks, such as video-language generation introduces two core challenges: (i) visual token inflation, where long visual token sequences restrict context capacity and result in insufficient feedback signals; (ii) a lack of process-level supervision, as existing methods focus on outcome-level supervision and overlook intermediate supervision, limiting prompt optimization. We present UniAPO: Unified Multimodal Automated Prompt Optimization, the first framework tailored for multimodal APO. UniAPO adopts an EM-inspired optimization process that decouples feedback modeling and prompt refinement, making the optimization more stable and goal-driven. To further address the aforementioned challenges, we introduce a short-long term memory mechanism: historical feedback mitigates context limitations, while historical prompts provide directional guidance for effective prompt optimization. UniAPO achieves consistent gains across text, image, and video benchmarks, establishing a unified framework for efficient and transferable prompt optimization.
Model-Based Testing of an Intermediate Verifier Using Executable Operational Semantics
Venue: arXiv cs.LO 2025
Year: 2025

Lightweight validation technique, such as those based on random testing, are sometimes practical alternatives to full formal verification -- providing valuable benefits, such as finding bugs, without requiring a disproportionate effort. In fact, they can be useful even for fully formally verified tools, by exercising the parts of a complex system that go beyond the reach of formal models. In this context, this paper introduces BCC: a model-based testing technique for the Boogie intermediate verifier. BCC combines the formalization of a small, deterministic subset of the Boogie language with the generative capabilities of the PLT Redex language engineering framework. Basically, BCC uses PLT Redex to generate random Boogie programs, and to execute them according to a formal operational semantics; then, it runs the same programs through the Boogie verifier. Any inconsistency between the two executions (in PLT Redex and with Boogie) may indicate a potential bug in Boogie's implementation. To understand whether BCC can be useful in practice, we used it to generate three million Boogie programs. These experiments found 2% of cases indicative of completeness failures (i.e., spurious verification failures) in Boogie's toolchain. These results indicate that lightweight analysis tools, such as those for model-based random testing, are also useful to test and validate formal verification tools such as Boogie.
A Defect Classification Framework for AI-Based Software Systems (AI-ODC)
Venue: arXiv cs.SE 2025
Year: 2025

Artificial Intelligence has gained a lot of attention recently, it has been utilized in several fields ranging from daily life activities, such as responding to emails and scheduling appointments, to manufacturing and automating work activities. Artificial Intelligence systems are mainly implemented as software solutions, and it is essential to discover and remove software defects to assure its quality using defect analysis which is one of the major activities that contribute to software quality. Despite the proliferation of AI-based systems, current defect analysis models fail to capture their unique attributes. This paper proposes a framework inspired by the Orthogonal Defect Classification (ODC) paradigm and enables defect analysis of Artificial Intelligence systems while recognizing its special attributes and characteristics. This study demonstrated the feasibility of modifying ODC for AI systems to classify its defects. The ODC was adjusted to accommodate the Data, Learning, and Thinking aspects of AI systems which are newly introduced classification dimensions. This adjustment involved the introduction of an additional attribute to the ODC attributes, the incorporation of a new severity level, and the substitution of impact areas with characteristics pertinent to AI systems. The framework was showcased by applying it to a publicly available Machine Learning bug dataset, with results analyzed through one-way and two-way analysis. The case study indicated that defects occurring during the Learning phase were the most prevalent and were significantly linked to high-severity classifications. In contrast, defects identified in the Thinking phase had a disproportionate effect on trustworthiness and accuracy. These findings illustrate AIODC's capability to identify high-risk defect categories and inform focused quality assurance measures.
WOMAC: A Mechanism For Prediction Competitions
Venue: arXiv cs.GT 2025
Year: 2025

Competitions are widely used to identify top performers in judgmental forecasting and machine learning, and the standard competition design ranks competitors based on their cumulative scores against a set of realized outcomes or held-out labels. However, this standard design is neither incentive-compatible nor very statistically efficient. The main culprit is noise in outcomes/labels that experts are scored against; it allows weaker competitors to often win by chance, and the winner-take-all nature incentivizes misreporting that improves win probability even if it decreases expected score. Attempts to achieve incentive-compatibility rely on randomized mechanisms that add even more noise in winner selection, but come at the cost of determinism and practical adoption. To tackle these issues, we introduce a novel deterministic mechanism: WOMAC (Wisdom of the Most Accurate Crowd). Instead of scoring experts against noisy outcomes, as is standard, WOMAC scores experts against the best ex-post aggregate of peer experts' predictions given the noisy outcomes. WOMAC is also more efficient than the standard competition design in typical settings. While the increased complexity of WOMAC makes it challenging to analyze incentives directly, we provide a clear theoretical foundation to justify the mechanism. We also provide an efficient vectorized implementation and demonstrate empirically on real-world forecasting datasets that WOMAC is a more reliable predictor of experts' out-of-sample performance relative to the standard mechanism. WOMAC is useful in any competition where there is substantial noise in the outcomes/labels.
Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Venue: arXiv cs.CL 2025
Year: 2025

Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate phonetic representation.
Information availability in different languages and various technological constraints related to multilinguism on the Internet
Venue: arXiv cs.CL 2025
Year: 2025

The usage of Internet has grown exponentially over the last two decades. The number of Internet users has grown from 16 Million to 1650 Million from 1995 to 2010. It has become a major repository of information catering almost every area. Since the Internet has its origin in USA which is English speaking country there is huge dominance of English on the World Wide Web. Although English is a globally acceptable language, still there is a huge population in the world which is not able to access the Internet due to language constraints. It has been estimated that only 20-25% of the world population speaks English as a native language. More and more people are accessing the Internet nowadays removing the cultural and linguistic barriers and hence there is a high growth in the number of non-English speaking users over the last few years on the Internet. Although many solutions have been provided to remove the linguistic barriers, still there is a huge gap to be filled. This paper attempts to analyze the need of information availability in different languages and the various technological constraints related to multi-linguism on the Internet.
Adaptive Learning for Moving Target defence: Enhancing Cybersecurity Strategies
Venue: arXiv cs.GT 2025
Year: 2025

In this work, we model Moving Target Defence (MTD) as a partially observable stochastic game between an attacker and a defender. The attacker tries to compromise the system through probing actions, while the defender minimizes the risk by reimaging the system, balancing between performance cost and security level. We demonstrate that the optimal strategies for both players follow a threshold structure. Based on this insight, we propose a structure-aware policy gradient reinforcement learning algorithm that helps both players converge to the Nash equilibrium. This approach enhances the defender's ability to adapt and effectively counter evolving threats, improving the overall security of the system. Finally, we validate the proposed method through numerical simulations.
"Nobody should control the end user": Exploring Privacy Perspectives of Indian Internet Users in Light of DPDPA
Venue: arXiv cs.HC 2025
Year: 2025

With the rapid increase in online interactions, concerns over data privacy and transparency of data processing practices have become more pronounced. While regulations like the GDPR have driven the widespread adoption of cookie banners in the EU, India's Digital Personal Data Protection Act (DPDPA) promises similar changes domestically, aiming to introduce a framework for data protection. However, certain clauses within the DPDPA raise concerns about potential infringements on user privacy, given the exemptions for government accountability and user consent requirements. In this study, for the first time, we explore Indian Internet users' awareness and perceptions of cookie banners, online privacy, and privacy regulations, especially in light of the newly passed DPDPA. We conducted an online anonymous survey with 428 Indian participants, which addressed: (1) users' perspectives on cookie banners, (2) their attitudes towards online privacy and privacy regulations, and (3) their acceptance of 10 contentious DPDPA clauses that favor state authorities and may enable surveillance. Our findings reveal that privacy-conscious users often lack consistent awareness of privacy mechanisms, and their concerns do not always lead to protective actions. Our thematic analysis of 143 open ended responses shows that users' privacy and data protection concerns are rooted in skepticism towards the government, shaping their perceptions of the DPDPA and fueling demands for policy revisions. Our study highlights the need for clearer communication regarding the DPDPA, user-centric consent mechanisms, and policy refinements to enhance data privacy practices in India.
A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces a holistic perception system for internal and external monitoring of autonomous vehicles, with the aim of demonstrating a novel AI-leveraged self-adaptive framework of advanced vehicle technologies and solutions that optimize perception and experience on-board. Internal monitoring system relies on a multi-camera setup designed for predicting and identifying driver and occupant behavior through facial recognition, exploiting in addition a large language model as virtual assistant. Moreover, the in-cabin monitoring system includes AI-empowered smart sensors that measure air-quality and perform thermal comfort analysis for efficient on and off-boarding. On the other hand, external monitoring system perceives the surrounding environment of vehicle, through a LiDAR-based cost-efficient semantic segmentation approach, that performs highly accurate and efficient super-resolution on low-quality raw 3D point clouds. The holistic perception framework is developed in the context of EU's Horizon Europe programm AutoTRUST, and has been integrated and deployed on a real electric vehicle provided by ALKE. Experimental validation and evaluation at the integration site of Joint Research Centre at Ispra, Italy, highlights increased performance and efficiency of the modular blocks of the proposed perception architecture.
DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Venue: arXiv cs.SE 2025
Year: 2025

Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reduced-order models (ROMs) derived from high-fidelity simulations of the PT's nominal behavior, and (ii) specialize these prototypes into DT instances by leveraging historical sensor data from the target PT. Despite the broad applicability of ML, its use in DT engineering remains largely ad hoc. Indeed, while conventional ML pipelines often train a single model for a specific task, DTs typically require multiple, task- and domain-dependent models. Thus, a more structured approach is required to design DTs. In this paper, we introduce DesCartes Builder, an open-source tool to enable the systematic engineering of ML-based pipelines for real-time DT prototypes and DT instances. The tool leverages an open and flexible visual data flow paradigm to facilitate the specification, composition, and reuse of ML models. It also integrates a library of parameterizable core operations and ML algorithms tailored for DT design. We demonstrate the effectiveness and usability of DesCartes Builder through a civil engineering use case involving the design of a real-time DT prototype to predict the plastic strain of a structure.
Topology Aware Neural Interpolation of Scalar Fields
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a neural scheme for the topology-aware interpolation of time-varying scalar fields. Given a time-varying sequence of persistence diagrams, along with a sparse temporal sampling of the corresponding scalar fields, denoted as keyframes, our interpolation approach aims at "inverting" the non-keyframe diagrams to produce plausible estimations of the corresponding, missing data. For this, we rely on a neural architecture which learns the relation from a time value to the corresponding scalar field, based on the keyframe examples, and reliably extends this relation to the non-keyframe time steps. We show how augmenting this architecture with specific topological losses exploiting the input diagrams both improves the geometrical and topological reconstruction of the non-keyframe time steps. At query time, given an input time value for which an interpolation is desired, our approach instantaneously produces an output, via a single propagation of the time input through the network. Experiments interpolating 2D and 3D time-varying datasets show our approach superiority, both in terms of data and topological fitting, with regard to reference interpolation schemes. Our implementation is available at this GitHub link : https://github.com/MohamedKISSI/Topology-Aware-Neural-Interpolation-of-Scalar-Fields.git.
Previously on... Automating Code Review
Venue: arXiv cs.SE 2025
Year: 2025

Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability in task definitions, datasets, and evaluation procedures. This study provides the first comprehensive analysis of MCR automation research, aiming to characterize the field's evolution, formalize learning tasks, highlight methodological challenges, and offer actionable recommendations to guide future research. Focusing on the primary code review tasks, we systematically surveyed 691 publications and identified 24 relevant studies published between May 2015 and April 2024. Each study was analyzed in terms of tasks, models, metrics, baselines, results, validity concerns, and artifact availability. In particular, our analysis reveals significant potential for standardization, including 48 task metric combinations, 22 of which were unique to their original paper, and limited dataset reuse. We highlight challenges and derive concrete recommendations for examples such as the temporal bias threat, which are rarely addressed so far. Our work contributes to a clearer overview of the field, supports the framing of new research, helps to avoid pitfalls, and promotes greater standardization in evaluation practices.